首页> 外文期刊>Palaeogeography, Palaeoclimatology, Palaeoecology: An International Journal for the Geo-Sciences >What settlements leave behind - pXRF compositional data analysis of archaeological layers from Tell el-Fara'in (Buto, Egypt) using machine learning
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What settlements leave behind - pXRF compositional data analysis of archaeological layers from Tell el-Fara'in (Buto, Egypt) using machine learning

机译:什么定居点留下了 - PXRF组成数据分析考古学层,从告诉El-Fara'in(Buto,埃及)使用机器学习

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摘要

Modern portable and handheld XRF devices (pXRF) allow quick measurement of large geochemical datasets without the necessity for laboratory facilities. Such facilities are rare in Egypt and modern dating techniques which are indispensable in Geoarchaeology to establish a robust chronology are not available, as sample transport is restricted and Egypt does not provide OSL or AMS C-14 laboratories. With these preconditions, we evaluate the usability of pXRF geochemical data for the dating of archaeological sediments with machine learning techniques. The sample material was collected via sampling of archaeological sections and profile walls from archaeological excavations in the northwestern Nile delta at the settlement site Buto (Tell el-Fara'in) and Kom el-Gir. Additionally, samples were taken from sediments and cultural layers uncovered from their surroundings using vibracore corings with open steel auger heads. In this methodological approach, we examine the applicability of pXRF methods and test the sample data for distinct geochemical differences between the main settlement phases with multivariate statistical methods. The dating is based on the training of artificial neural networks with pXRF data from archaeological material of well-dated context to date test data of cultural layers within the vibracores. This allows us to link fundamental changes in the landscape with the settlement history of Buto and neighboring tells.
机译:现代便携式和手持式XRF设备(PXRF)允许快速测量大型地球化学数据集,而无需实验室设施。这些设施在埃及和现代约会技术中都是罕见的,即GeoAthaeogy中不可或缺地建立强大的年表,因为样品运输受到限制,埃及不提供OSL或AMS C-14实验室。通过这些前提条件,我们评估了PXRF地球化学数据的可用性,以了解机器学习技术的考古沉积物的约会。通过在沉降地点的西北尼罗河三角洲的考古挖掘中采样来收集样品材料,在沉降地点(告诉El-Fara'in)和Kom El-Gir。另外,使用具有开放式钢螺旋头的Vibracore镶嵌,从沉积物和栽培层中取出样品。在这种方法论方法中,我们研究了PXRF方法的适用性,并测试了具有多元统计方法的主要沉降阶段之间的不同地球化学差异的样本数据。该约会基于对人工神经网络的培训,利用富日期地区的考古学材料与vibracory内的培养层的日期测试数据。这使我们能够通过Buto和邻国的结算历史将景观中的基本变化联系起来。

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